Responsibilities
- Develop and implement machine learning models for identifying image-based fraud, such as deepfakes, injection attacks, and altered biometric data.
- Manage the complete ML workflow, from gathering and refining adversarial datasets to deploying and monitoring models in production.
- Construct scalable data pipelines with emphasis on data validation, cleansing, labeling, and handling noisy inputs.
- Address data challenges including class imbalance, bias mitigation, and adapting to shifting data distributions.
- Establish evaluation strategies that prioritize real-world effectiveness, including precision-recall optimization and false positive control.
- Ensure deployed systems are resilient, scalable, and continuously updated to counter emerging fraud techniques through close collaboration with engineering and research teams.
Benefits
- Competitive compensation package
- Fully remote work arrangement
- Paid annual leave
- Allowance for home office setup
- Performance-based annual bonus of up to 10%
- Comprehensive health insurance coverage
- Support for continuous learning with access to LinkedIn Learning and role-relevant development programs
Compensation
Competitive package with annual bonus up to 10%
Work Arrangement
Full Remote
Team
Collaborative environment involving AI research, data operations, MLOps, platform engineering, and product teams
Other
Occasional in-person meetings, ideally on a weekly or biweekly basis, may be required.
